This invention relates to data mining and in particular to evaluating data from a database producing results similar to those of using on-line analytical processing (OLAP) but in a far more computationally efficient manner.
In problems such as in extracting market data from a database, data is often organized in dimensions that are in a hierarchy. For example, records are often assigned ID's and the records will have data for various attributes that a user may wish to track. An example of a dimension hierarchy might be age. The hierarchy of age can have levels as young, middle, and old. Within each of these levels of young, middle and old can be various numeral age groupings or sublevels such as young being 18-25 or 25-30; middle being 30-40 and 40-55; and old being 55-65 and 65 and over, and so forth. A second hierarchy might be income, with income having different levels and sublevels. Competing approaches to evaluate cross tabulations of age and income in this example use techniques where the number of computations is related to the number of dimensions and number of levels or sublevels of the data. For very complex or large number of dimensions, the computations increase at an exponential rate.